eventdisplay-ml 0.1.1

A machine learning toolkit for Eventdisplay

Machine learning for Eventdisplay

Toolkit to interface and run machine learning methods together with the Eventdisplay software package for gamma-ray astronomy data analysis.

Provides examples on how to use e.g., scikit-learn or XGBoost regression trees to estimate event direction, energies, and gamma/hadron separators.

Introduces a Python environment and a scripts directory to support training and inference.

Direction and energy reconstruction using XGBoost

Stereo analysis methods implemented in Eventdisplay provide direction / energies per event resp telescope image. The machine learner implemented Eventdisplay-ML uses XGB Boost regression trees. Features are all estimators (e.g. DispBDT or intersection method results) plus additional features (mostly image parameters) to get a better estimator for directions and energies.

Input is provided through the mscw output (data trees).

Output is a single ROOT tree called StereAnalysis with the same number of events as the input tree.

Citing this Software

Please cite this software if it is us ed for a publication, see the Zenodo record and CITATION.cff for details.